Providing a result with a requested accuracy using individuals previously acting with a consensus

ABSTRACT

A result for a task may be provided in response to receiving a request from, for example, a user for the result. The request specifies a desired accuracy level for the result. The accuracy of the result is determined using the individual accuracies of one or more persons that have selected the result. Each person&#39;s individual accuracy is determined based on results for prior tasks previously performed by that person. The person&#39;s individual accuracy is proportional to the number of that person&#39;s prior results that are with a consensus of other persons that have performed the same prior task. The result is provided if the accuracy of the result is equal to or greater than the desired accuracy level.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of, and claims priorityto, U.S. patent application Ser. No. 13/539,152, filed Jun. 29, 2012,entitled “Providing a Result with a Requested Accuracy Using IndividualsPreviously Acting with a Consensus”, by Byron Reese and William Ballard,which is a continuation application of U.S. patent application Ser. No.12/706,927, filed Feb. 17, 2010, entitled “Providing a Result with aRequested Accuracy Using Individuals Previously Acting with aConsensus,” by Byron Reese and William Ballard, now U.S. Pat. No.8,290,812, the entire contents of which application is incorporated byreference as if fully set forth herein.

FIELD OF THE TECHNOLOGY

This disclosure relates generally to data processing systems, and morespecifically to methods, systems and machine-readable mediums forproviding a result in response to a request in which the result has anaccuracy determined based on the individual accuracies of one or morepersons that have acted with a consensus in prior activities.

BACKGROUND

Business processes often include a large number of tasks that need to beaccomplished with varying degrees of accuracy. Some of these tasks mayinclude, for example, the selection of keywords that are relevant for agiven topic, the keying in of phone numbers or other data entry, and thetranscription of medical records.

In performing a task, people typically do not achieve perfect accuracy(e.g., some members of a large group will fail to select a correctanswer from a set of choices, or a given person will sometimes fail afew times when repeating a tedious task many times). Instead, peopleinevitably will make some mistakes, even with relatively simple tasks.The error rates for a typical person in the performance of a particulartask might be, for example, 5-10% (i.e., the person is 90-95% accurateover many attempts at the same task). The error rate (or accuracy)depends upon, among other factors, training and the particular type oftask performed.

BRIEF DESCRIPTION OF THE DRAWINGS

Various features of the present disclosure will become more apparentwith reference to the following description taken in conjunction withthe accompanying drawings wherein like reference numerals denote likeelements and in which:

FIG. 1 is an exemplary block diagram of a system for providing a resultto a user satisfying a desired accuracy level requested by the user,according to one embodiment of the present disclosure.

FIG. 2 is an exemplary flowchart outlining the operation of the resultmodule of FIG. 1, according to one embodiment of the present disclosure.

FIG. 3 is an exemplary flowchart outlining the operation of the feedbackmodule of FIG. 1, according to one embodiment of the present disclosure.

FIG. 4 is an exemplary screen shot illustrating a task of selecting akeyword result by two initial persons to match a predetermined title,according to one embodiment of the present disclosure.

FIG. 5 is an exemplary screen shot illustrating the task of selecting akeyword result by an additional third person to act as a tie breaker,according to one embodiment of the present disclosure.

FIG. 6 is an exemplary screen shot illustrating the task of selecting anassessment result to assess a title being considered for potential usewith a content publication, according to one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

In the description that follows, the present disclosure may be describedin reference to one or more embodiments for providing a result (e.g., toan end user or to a software process executing on a computer system) ifthe result is determined to have an accuracy level equal to or greaterthan a requested accuracy level. The present disclosure, however, is notlimited to any particular application, nor is it limited by the examplesdescribed below. Various modifications to the disclosed embodiments maybe apparent to those skilled in the art and the general principlesdefined herein may be applied to other embodiments and applicationswithout departing from the spirit and scope of the disclosure.Therefore, the description of the embodiments that follow are forpurposes of illustration and not limitation.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, or the like means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, various features are describedwhich may be exhibited by some embodiments and not by others. Similarly,various requirements are described which may be requirements for someembodiments but not other embodiments.

The disclosure below generally provides systems, methods andmachine-readable mediums for providing a result corresponding to aparticular task (e.g., the task of selecting a word or title from a listof words or titles, with the result being the selected word or title).The systems may include a storage device and a processor. The computerreadable mediums provide instructions to cause the processor to performthe operations described herein.

A request (e.g., a request from a person at a user terminal, a requestfrom a software process, or an automated request from another computingsystem) is received that asks that a result be provided (e.g., providinga selected title from a list of titles). The request specifies a desiredaccuracy level for the result. The accuracy of the result is determinedusing an individual accuracy of one or more persons that select theresult, as discussed in more detail below. For example, if two or threehighly-accurate individuals select the same title from a list ofpossible titles to choose from, then that same selected title is likelyto be a sufficiently accurate result that can be provided in response toa user's request. In general, the previously-demonstrated accuracy ofone, two, or more individuals is relied upon or used for making theselection of a new title requested by the user.

Each person's individual accuracy is determined based on prior resultsto prior tasks previously performed by that person. For example, a“batting record” or voting history is recorded for each person based onhow that person acts or votes relative to other persons performing thesame prior task (i.e., whether the person acts with or against aconsensus). Each person's individual accuracy is proportional to thenumber of that person's prior results that are with a consensus of otherpersons that have performed the same prior task (e.g., a person has ahigher individual accuracy if that person consistently votes or actswith the consensus of persons performing the same task). The result isprovided if the accuracy determined for the result is equal to orgreater than the desired accuracy level that was initially requested.Various embodiments and features of the present disclosure will beapparent from the accompanying drawings and from the detaileddescription which follows.

FIG. 1 is an exemplary block diagram of a system 10 for providing aresult to a user (or alternatively another computer process or system)that satisfies a desired accuracy level requested by the user, accordingto one embodiment of the present disclosure. The system 10 may include acomputing device 12, a content source 14, a plurality of feedbackprovider terminals 16, and at least one user terminal 18. The componentsof the system 10 may be distributed over a network 20 (e.g., computingdevice 12 may include several processors, or more than one computingdevice 12 may be used to perform various functions described herein).

The network 20 may be an internet or intranet, or a combination thereof.For example, the components of the system 10 may be selectivelydistributed over the Internet as well as maintained within an intranetof an organization. In one embodiment, the terminals 16 and 18 may runcommercially-available Web browser applications such as MicrosoftInternet Explorer®, which implements World Wide Web standards such asHTTP, HTML, XML, java, Flex, Ajax and the like.

In one embodiment, the computing device 12 may include a processor 26,one or more modules, and one or more databases. For example, thecomputing device 12 may include a storage device 24, a result module 22,and feedback module 28. The processor 26 may be a commercially-availableprocessor that accesses the content source 14 and/or dynamicallygenerates Web pages in response to end user actions (e.g., as requestedby an end user of a cell phone or personal digital assistant). In someembodiments, the computing device 12 may include a processor withmultiple processing cores.

The Web pages may be in the form of HTML pages or the like. For example,the Web pages generated may include a result requested by the user ifthe result has an accuracy equal to or greater than a requested accuracylevel. The Web page may include content materials (e.g., related to theresult being provided).

In one embodiment, the Web page may be provided by result module 22 to,for example, a user that is operating user terminal 18. The web page maybe used, for example, to present the result to the user (along withother information, such as the actual accuracy determined by resultmodule 22 for the result being provided). If the desired accuracy levelcould not be achieved, the actual accuracy determined may be provided inthe web page, along with other statistics on individuals used todetermine the accuracy.

For example, the user may have initially made a request for a title tobe used with an article that the user intends to create and publish. Theuser may have requested that the title have an accuracy level of, forexample, 90%. The web page may present the result to the user in theform of a selected title that the user can use for the contentpublication (e.g., article) that the user intends to create. Theselected title may have an accuracy of, for example, 93% as determinedby result module 22. The web page may present this accuracy to the useralong with the title.

The title is presented to the user here because it has an accuracy(i.e., 93%) that is greater than the accuracy requested by the user. Ifcomputing device 12 were not able to calculate an accuracy for any titlethat was at least 90% as requested, then result module 22 would, forexample, notify the user in the web page that an acceptable title is notavailable.

In one embodiment, feedback module 28 is used to determine individualaccuracies for a group of persons. In one embodiment, these persons may,for example, have previously performed a task similar to or the same asthat for which the user of user terminal 18 is requesting a result(i.e., the persons may have prior experience in selecting suitabletitles for content publications, and the persons may have selected suchtitles with the consensus in most situations).

In one embodiment, a number of individuals are feedback providers usingfeedback provider terminals 16. The feedback providers may be, forexample, employees or contractors of an entity that operates computingdevice 12. Feedback module 28 may be used to send surveys to thesefeedback providers. Feedback from each survey is used to develop anindividual “batting record” or activity history for each individual.These activity histories are used to calculate individual accuracies, asdiscussed further below, and may be stored in storage device 24.

If an individual votes with the consensus for a given survey, then thatindividual is considered to have voted accurately. That individual'sperformance over a number of such surveys is used to calculate theindividual's accuracy. For example, if the individual votes with aconsensus in 80 out of 100 surveys, then the individual's accuracy is80%. Individual accuracies are similarly determined for the otherfeedback providers. These individual accuracies are then used by resultmodule 22 to calculate an accuracy for a new result being requested(e.g., requested by an end user or customer on user terminal 18). Forexample, the feedback providers may be employees that a service companyhas on staff for use in rapidly responding to a request from a customeron user terminal 18 (e.g., logged into a web site for the servicecompany).

In one embodiment, the result module 22 and the feedback module 28 maybe implemented together in the computing device 12, as shown in FIG. 1.Alternatively, the result module 22 and the feedback module 28 may beimplemented in separate computing devices coupled locally or remotelyover the network 20.

In an alternative embodiment, if the accuracy of a result is less than adesired accuracy level, the plurality of choices is discarded, and aresult is selected from a new set of choices. The result from the newset is provided if the desired accuracy level can be satisfied. Forexample, the initial plurality of choices may be an initial list ofpotential titles for a content item. The new set of choices may be a newlist of potential titles for the content item. The new list may differfrom the initial list by one or more titles. Also, the new list may becompletely different from the initial list.

In one embodiment, any portion of the storage device 24 can be providedexternally from the computing device 12, either locally to the computingdevice 12 or remotely over the network 20. The external data from anexternal storage device can be provided in any standardized form whichthe processor 26 can understand. For example, an external storage deviceat a provider can advantageously provide content material in response torequests from the processor 26 in a standard format, such as, forexample, images, videos, audios, text-based material and the like, whichthe processor 26 may then transform into a function call format that thecode module(s) can understand. The processor 26 may be used to provide astandard SQL server, where dynamic requests from the server builds formsfrom one or more databases used by the computing device 12 as well asstores and retrieves related data on the storage device.

As can be appreciated, the storage device 24 may be used to store,arrange and retrieve data. The storage device 24 may be amachine-readable medium, which may be any mechanism that provides (e.g.,stores) information in a form readable by a processor. For example, themachine-readable medium may be a read only memory (ROM), a random accessmemory (RAM), a cache, a hard disk drive, a floppy disk drive, amagnetic disk storage media, an optical storage media, a flash memorydevice or any other device capable of storing information. Additionally,a machine-readable medium may also comprise computer storage media andcommunication media. A machine-readable medium may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. A machine-readable medium may also include, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computer.

As shown in FIG. 1, the content source 14 may be an external source forstoring and retrieving content material and be remotely accessible overthe network 20. In one embodiment, the content source 14 may also be aninternal source maintained in the computing device 12 or locally coupledto the computing device 12. In one embodiment, the content source 14 mayconstitute a plurality of smaller sources, or databases, that in theaggregate are represented by the content source 14. The content source14 may be managed or maintained by third party entities different fromthose managing the computing device 12 or components thereof. Thecontent material stored and retrieved from the content source 14 mayinclude, but is not limited to, images, videos, audios, text-basedmaterial, and/or links or hyperlinks to images, videos, audios,text-based material and the like.

FIG. 2 is an exemplary flowchart outlining a process 200 for theoperation of result module 22, according to one embodiment of thepresent disclosure. In block 202, a request is received from a user ofuser terminal 18 requesting that a result be provided (e.g., for a tasksuch as selecting a best title from a list of title choices). Therequest specifies a desired accuracy level (e.g., that the accuracy beat least 90%).

In block 204, result module 22 requests that one or more individualsoperating feedback provider terminals 16 make a selection of a result(e.g., select a title from a list of titles) in response to the requestfrom user terminal 18. Each individual is presented with a set ofchoices (e.g., 3-4 title choices) and then selects one of the choices.The individual in some cases may use a predefined set of standards touse in making the selection (e.g., standards or guidelines for valuableor desirable titles previously provided to and reviewed by employees ofan online media services company).

In some embodiments, these individuals will be paid compensation foreach selection made (e.g., working as a contractor). In one embodiment,a cost may be associated with the selection of a result by each of theindividuals. The sum of the costs for all individuals is a total costfor providing the result. Additional individuals can be used to makeselections in an attempt to achieve the desired accuracy level. However,repeatedly adding further individuals may be stopped if the total costreaches a predetermined maximum (e.g., a maximum cost of $0.50).

For example, if two individuals each have an individual accuracy of 80%,and both individuals select the same “Title B” from a list of titles“Title A, Title B, Title C, and Title D,” then result module 22determines an accuracy of 96% for the Title B result. This is greaterthan the requested accuracy level of 90%, so result module 22 providesTitle B as a result to user terminal 18 via network 20. Alternatively,if the user had requested an accuracy level of 98%, then result module22 would ask additional individuals on terminals 16 make a titleselection in an effort to reach the 98% requested accuracy.

It should be noted that in alternative embodiments, the individuals mayhave made selections of results prior to receiving a request from theuser. In this case, result module 22 uses preexisting records of one ormore individuals in an attempt to satisfy the new request and desiredaccuracy level. Each such record may, for example, include a title orother result previously selected by that individual in response to atask.

With respect to the accuracy for various types of tasks or activities,the accuracy of a result is determined based on the selections oractions of individuals using individual accuracies as determined fromtheir prior consensus activities as described herein. For some tasks,there will be an objectively correct answer. For other tasks, there willbe a reasonably correct answer (e.g., as determined by the majority ofpeople in a given group or society or particular social network), butthe degree of objectivity may be less clear. For yet other tasks, theremay be an increased subjective aspect to an “accurate” result. Theapproaches described herein may be used in general with all of theforegoing and other forms of accuracy.

In block 206, if the accuracy of the result is determined to be at leastequal to the desired accuracy level that was initially requested, thenresult module 22 provides the result to the user. In some cases,particularly as the requested accuracy level increases (e.g., 99%), itwill not be practical (or it may even be impossible) to provide theresult as the desired accuracy cannot be achieved, even when using alarge number of individuals. In some embodiments, result module 22 willstop adding additional individuals after reaching a predetermined numberof individuals already used (e.g., stop after using 10 individuals). Insome embodiments in which individuals are compensated for providingselections, this limiting of the number of individuals is done tocontrol the expense of providing a result.

As can be appreciated, it is typically more difficult to obtain a resultas the desired accuracy level increases. In some embodiments, the useror other requestor of a result pays a fee to the operator of computingdevice 12 based on the requested accuracy level, with the fee beinggreater for a higher desired accuracy level.

In some embodiments, empirical data may be collected in order todetermine the difficulty of obtaining a consensus in a given type ofsituation. For some tasks, it is easy to get a consensus (e.g., whichcolor is the brighter of two choices). The data may show, for example,that 20% of the time a consensus is obtained using two persons (i.e., intwo votes), 40% of the time in three votes, and that 60% of the time,four or more people are necessary to obtain a consensus. From this data,a cost model may be determined (e.g., the number of times the title ispresented multiplied by the cost (e.g., 5 cents) that is paid each timethe title is shown to a person).

FIG. 3 is an exemplary flowchart outlining a process 300 for theoperation of feedback module 28, according to one embodiment of thepresent disclosure. As discussed above, result module 22 uses individualaccuracies of persons or feedback providers in order to calculate ordetermine an accuracy for a new or current result that may be providedto a user at user terminal 18 or to another requesting person, entity,process, or computer system. The individual accuracies may be stored asa plurality of records in storage device 24. The plurality of recordsare typically created prior to receiving the request for a result.

In one embodiment, the individual accuracies are determined by process300. In block 302, a survey is sent to feedback devices or terminals 16(e.g., personal digital assistants, computer workstations, or cellphones). The survey includes a plurality of data items (e.g., a list oftitles or images) and each feedback device 16 is operated by a person orfeedback provider.

In block 304, feedback is received from each feedback device 16. Thefeedback associates an activity at each respective feedback device 16with at least one of the data items. The feedback provider may selectone data item as being, for example, the correct choice, the bestchoice, or the most-desirable choices. As mentioned above, the feedbackprovider may use standards for selection that are defined by anotherentity and/or reviewed by the feedback providers prior to the survey.All of the survey responses are collected for analysis by feedbackmodule 28.

In block 306, the feedback (i.e., the selections by the feedbackproviders) are used to determine a consensus selection or result for thesurvey. In some embodiments, the consensus result is the result selectedby the majority of feedback providers. Each individual's selection isanalyzed to determine whether the individual acted or selected with theconsensus or against the consensus.

In some embodiments, one or more individuals may be identified as havinga low individual accuracy. For example, if the individual is anemployee, then the cost of employing the individual may exceed therevenue generated based on accurate choice selections. In this case, theuse of the employee in choice selections may be ended.

In other embodiments, whether or not the individual is paid anycompensation, an individual may no longer be used in further accuracydeterminations if the individual accuracy of the individual falls belowa minimum individual accuracy threshold. This cessation of use may betemporary or permanent. In alternative embodiments, it has been foundthat some individuals may have a high accuracy in some types of tasks,but not in other types of tasks. These tasks may be determined fromobservation of the individual or from empirical data. The individual maybe used only for those types of tasks in which the individual has a highor acceptable accuracy.

In block 308, the plurality of records is created so that each recordcorresponds to one of the individuals or feedback providers. Each recordindicates whether an activity of the feedback device 16 operated by thatindividual is with the consensus.

Additional surveys are performed in which additional activity orselection feedback is obtained with respect to each individual orfeedback provider. An individual's accuracy is determined based on thepercentage of surveys in which the individual acted with the consensus.For example, an individual accuracy of 90% means that the individualacted with the consensus in 9 of 10 surveys performed.

In some embodiments, the items provided to feedback providers in asurvey may be a list of two or more content materials (e.g., the contentmaterials may be displayed to the feedback provider on a display). Thefeedback module 28 may provide code instructions to transmit a pluralityof content materials to a plurality of feedback provider terminals 16,and query the plurality of feedback provider terminals 16 to identify atleast one content material from the plurality of content materials thatis being selected in the survey. The feedback module 28 may then receivea feedback, from each of the plurality of feedback provider terminals16, identifying the at least one content material being selected. In oneembodiment, the feedback module 28 may then evaluate the feedback, fromeach of the plurality of feedback provider terminals 16, to determinethe individual accuracies. In one embodiment, a consensus may be basedon the frequency of at least one content material being selected by theplurality of feedback provider terminals 16.

In one example, an individual accuracy may have been determined to be80%. However, even given a mathematical accuracy for an individual, theconfidence in this accuracy may be low if the number of prior events orsurveys is small. The individual may be run through additional trialtasks that are not used to provide results to customers in order todevelop an improved history for that individual with a higherconfidence. For example, an employee may be paid to provide the answersin these trial surveys, but those answers are only used for calculatingan updated individual accuracy. For example, an employee might be runthrough about 100 problems in order to measure how often this individualvotes with the consensus. At this point, there is a sufficient historyand an accuracy may be established for that person with higherconfidence.

As can be appreciated, the feedback module 28 may provide codeinstructions to store the individual accuracies in the storage device24. In one embodiment, the feedback module 28 may also provide codeinstructions to store the feedback, from each of the plurality offeedback provider terminals 16, in the storage device 24.

The feedback module 28 may be operatively associated with the resultmodule 22 to provide a response to any request that is received for aresult. The result module 22 may receive the request from the at leastone user terminal 18. In response to the request, the feedback module 28may provide code instructions to retrieve the individual accuracies fromthe storage device 24 for use by result module 22. The result module 22may then provide a result to the at least one user terminal 18.

As an example of one type of survey, a list of content materials (e.g.,keywords or images) may be provided in a survey. The task for thefeedback providers is to select the content material(s) that is bestassociated with a proposed title for an online publication. For example,for a given title of “Apple,” the feedback module 28 may transmitcontent material associated with the fruit apple and the softwarecompany Apple, Inc. The feedback module 28 may then query the pluralityof feedback provider terminals 16 to select, vote, rank and/or identifyat least one choice from the plurality of content materials that is bestassociated with the title “Apple.” In one embodiment, the feedbackmodule 28 may query the plurality of feedback provider terminals 16 toselect only a single one of the content materials from the plurality ofcontent materials as being associated with the title “Apple.”

In one embodiment, the feedback module 28 may be programmed with codeinstructions to receive a feedback, from each of the plurality offeedback provider terminals 16, identifying the at least one contentmaterial that has been selected and to store the feedback for each ofthe plurality of feedback provider terminals 16 in the storage device24. In one embodiment, the feedback providers may be professionals,writers, publishers, critics, other individuals and/or entities whoprovide a feedback, via the feedback provider terminals 16, thatidentifies the at least one content material being selected by thatindividual. In alternative embodiments, the feedback may be data orother information provided from a software process or machine (e.g.,automatically provided with or without an explicit instruction from aperson). For example, the operation itself of a cell phone, PDA or othercomputing device may imply or be associated with selections that thencan be collected as feedback, and used to define a consensus and tocalculate individual accuracies.

In one embodiment, individual accuracies as determined from the feedbackmay be based on the frequency of at least one content material beingselected by the plurality of feedback provider terminals 16. Forexample, the individual accuracies may be determined based on aconsensus voting received from the feedback provider terminals 16.

As can be appreciated, the result module 22 of the computing device 12may be programmed with code instructions to receive a request from atleast one user terminal 18, and then provide a result as discussedabove. The present disclosure may be used for various applications toprovide a result (e.g., to provide a selection or decision as a resultto a user when the user is faced with or handling a problem having morethan one choice available). These various applications include, but arenot limited to, selecting a correct answer from a set of possible orpresented choices, selecting the best keyword search terms to use (e.g.,in association with an online content publication), selecting correctgrammar for a title (e.g., from a set of the most likely or mostreasonably possible choices), selecting the best matching of a title toa topic, selection of the best or the most correct photos to accompany atitle, the keying in of phone numbers or other data entry, thetranscription of medical records, and/or selection of a best or correcteditorial category for a title. In other embodiments, the task to beperformed could be the selection of a favorite service provider (e.g.,restaurant) or product. The set of possible choices could be determinedfrom various information databases (e.g., an online yellow pagesdirectory for a particular geographic location).

FIG. 4 is an exemplary screen shot 400 illustrating a task of selectinga keyword result by an initial two persons to best match a predeterminedtitle, according to one embodiment. More specifically, here a list ofchoices is presented in screen shot 400 (e.g., a web page) to each oftwo feedback providers in which the list includes a set of variousphrases 404, 406, 408, and 410 for potential use as keywords for a title402. One of the choices 410 is “None of the above.” In this example, thetwo feedback providers, each previously determined to have an individualaccuracy of 80% as stored in records a database, select a choice. Aresult (i.e., a title) has been requested (e.g., so that the titleresult can be used in later steps of a title quality assurance process)having a desired accuracy level of 90%. If the two feedback providerseach select choice 404, then this result is determined to have anaccuracy of 96% (i.e., calculated as [1−(0.20×0.20)]×100%) and isprovided as the requested result due to the accuracy being greater than90%.

If the two feedback providers do not agree on the best matching keyword,then the set of choices is sent to a third feedback provider (to act asa “tie breaker”). The third feedback provider also has an individualaccuracy, which is used along with the individual accuracies of theinitial two feedback providers to calculate an accuracy for the choiceselected by the third feedback provider. If this accuracy is greaterthan the desired accuracy level, then the choice selected by the thirdfeedback provider is provided as the requested result.

It should be noted that as the desired accuracy level increases, therewill be more cases in which the addition of the third feedback providerwill not “break the tie” (i.e., the desired accuracy level is notreached). In such a case, additional feedback providers may be presentedwith the set of choices, or a reduced set of choices, in an attempt toreach the desired accuracy level. In one embodiment, each choice of thereduced set of choices has previously been selected by a prior feedbackprovider.

FIG. 5 is an exemplary screen shot 500 illustrating the task ofselecting a keyword result by adding an additional third person, to actas a tie breaker, according to one embodiment. Here, two choices 504 and506 are presented for selection to select the best or correct choice ofa keyword phrase for a title 502. Two prior feedback providers did notagree on the same choice (e.g., having been presented with three or morechoices). The two different choices selected by the prior feedbackproviders are choices 504 and 506, which are presented to the “tiebreaker” person. After the tie breaker makes a selection, the choice isprovided as the result if the accuracy is determined to be sufficientlyhigh.

Here, it should also be noted that the feedback provider has anindication 508 (e.g., an earnings display box) of compensation orearnings that have been received for a current user session. Forexample, the feedback provider may be paid a certain monetary amount perchoice selected (e.g., $0.01 per selection).

FIG. 6 is an exemplary screen shot 600 illustrating the task ofselecting an assessment result to assess a title 602 being consideredfor potential use with a content publication, according to oneembodiment. Choices 604, 606, and 608 are presented on, for example, aweb page. Each choice is an assessment of title 602. The assessment maybe based on predefined standards or guidelines for a suitable title. Thefeedback provider may make selections based on these standards orguidelines. If the result is “Edit” 606, then after editing, the editedtitle may then be submitted to a new set of feedback providers fordetermining whether the title is acceptable (e.g., for publication).

Several non-limiting examples of the determination of an accuracy for aresult are provided below. These examples are intended to illustratespecific embodiments, and are not intended to limit the generality ofthe foregoing discussion.

In a first example, the situation of determining an accuracy level fromindividual persons (e.g., feedback providers) is described. Consider asimple situation of two choices being presented for selection: choice A(correct) and choice B (incorrect).

The required input parameter values are as follows:

-   -   ρ=probability that an individual person picks A (ρε(0, 1))    -   α=desired accuracy level (αε(0, 1))    -   c=cost per rating    -   C=maximum cost allowed per problem=cS (where S=maximum number of        ratings per problem)

Here, it is assumed that the person's individual accuracy or quality (asmeasured by the parameter ρ) is the same for all persons. Thisassumption may be relaxed in other embodiments to allow for a procedurethat “kicks out” poor-quality persons (persons with an individualaccuracy that falls below a minimum individual accuracy threshold).

The maximum number of ratings per problem S needs to be specified inorder to make this problem feasible. Even with disagreements among thepersons used to make the initial selections of choices (e.g., the firstthree or four persons), allowing for an unlimited number of personsrequires the analysis to allow for the small probability that aconsensus would be reached with a much larger number of persons.

Now consistent with this approach, individual persons are continued tobe asked for their selection of a choice until either:

-   -   1. The probability of the consensus choice being correct is        greater than α (in this case, accept the consensus choice and        the cost is equal to c×number of persons).    -   2. It is impossible to achieve the desired accuracy α within S        steps (in this case, the question is discarded or “thrown out”        and the cost is equal to c×number of persons).

The output from the approach (given values of the parameters (ρ, α, c,S)) is as follows:

-   -   1. The percentage of questions that are answered (not thrown        out).    -   2. The complete distribution of costs, which allows computation        of the average cost.

Changing the accuracy level α permits making a graph of these outputsversus α.

Regarding the implementation of the above approach, here are a fewexemplary iterations to illustrate the above formulas:

1. First Person

If ρ>α, accept the choice. Cost=c.If ρ<α, ask another person.

2. Second Person

If two persons agree and

${\frac{\rho^{2}}{\rho^{2} + \left( {1 - \rho} \right)^{2}} > \alpha},$

accept the choice. Cost=2c.If two persons agree and

${\frac{\rho^{2}}{\rho^{2} + \left( {1 - \rho} \right)^{2}} > \alpha},$

ask another person.

If two persons disagree and the desired accuracy level can still beachieved (this is a condition that depends upon ρ and S), ask anotherperson.

If two persons disagree and the desired accuracy level cannot beachieved, throw out the question. Cost=2c.

3. Third Person

If unanimous and

${\frac{\rho^{3}}{\rho^{3} + \left( {1 - \rho} \right)^{3}} > \alpha},$

accept the choice. Cost=3c.If not unanimous and accuracy level can still be achieved (depending onρ and S), ask another person.If not unanimous and accuracy level cannot be achieved, throw out thequestion. Cost=3c.

4. Fourth Person

If unanimous and

${\frac{\rho^{4}}{\rho^{4} + \left( {1 - \rho} \right)^{4}} > \alpha},$

accept the choice. Cost=4c.If 3-1 vote and

${\frac{\rho^{2}}{\rho^{2} + \left( {1 - \rho} \right)^{2}} > \alpha},$

accept the consensus choice. Cost=4c.If not unanimous and accuracy level can still be achieved (depending onρ and S), ask another person.If not unanimous and accuracy level cannot be achieved, throw out thequestion. Cost=4c.

5. Fifth and Additional Persons

Continue the above approach until the S-th person.

The probabilities of each of the cases in the approach above may bedetermined. With these probabilities, the outputs (e.g., percentage ofquestions that are accepted, or distribution of costs) may becalculated.

In a second example, there is a consideration of the extent ofconfidence associated with a selection or answer as different personsare answering a question, and of the cost required to obtain any givenlevel of accuracy. For example, to obtain a 99.9% accuracy, answers frommany persons will likely be discarded (i.e., the cost of a correctanswer is related to the incorrect answers that may need to be tossedout). Related to the foregoing, a person with a 90% individual accuracytypically should be paid more than a person with an 80% accuracy. Forexample, a person with a 60% accuracy should not be paid, and a personwith a lower accuracy should be dismissed.

Now considering achieving accuracy levels from individual persons, againconsider the simple situation of two choices being presented forselection: choice A (correct) and choice B (incorrect).

The required input parameter values are as follows:

-   -   ρ=probability that an individual person picks A (ρε(0, 1))    -   α=desired accuracy level (αε(0, 1))    -   c=cost per rating    -   C=maximum cost allowed per problem=cS (where S=maximum number of        ratings per problem)

One aspect of this simple two-choice situation is now described.Specifically, if R persons have been polled so far with R_(maj) pickingone choice and R_(min) picking the other choice (with R_(maj)≧R_(min)and R_(maj)+R_(min)=R), then the probability that the majority choice(the one with R_(maj) persons) is the correct choice is equal to

$\frac{\rho^{{Rmaj} - {Rmin}}}{\rho^{{Rmaj} - {Rmin}} + \left( {1 - \rho} \right)^{{Rmaj} - {Rmin}}}$

The accuracy level depends only upon the vote difference for the twochoices. As an example, suppose that ρ=0.75 (persons pick the consensusor right choice 75% of the time). When the vote difference is equal toone (which happens at votes of 1-0, 2-1, 3-2, etc.), the accuracy levelis 0.75. When the vote difference is equal to two (which happens atvotes of 2-0, 3-1, 4-2, etc.), the accuracy level is 0.75²/(0.75²+0.25²)or 0.90. With p=0.75, we can summarize the accuracy levels for votedifferences up to 4 in the following table:

R_(maj) − R_(min) Prob. correct 0 0.500 1 0.750 2 0.900 3 0.964 4 0.988

There is a natural “discontinuity” in this situation. To achieve anaccuracy level of 75%, one may stop when one choice has a one-voteadvantage (which trivially occurs after the first person's decision). Toachieve an accuracy level of 80%, a one-vote advantage is not enough, soone may stop when one choice has a two-vote advantage. The two-voteadvantage yields a 90% accuracy level (more than the desired accuracylevel). So, one does not ever get an accuracy level of 80%. The onlyachievable accuracy rates are those reflected in the table above: 75%,90%, 96.4%, and 98.8%.

Now an illustration of the algorithm's output is provided as follows:

ρ=0.75 (person efficiency 75%)α=0.95 (desired accuracy 95%)

From the discussion above, one should expect an accuracy of 96.4% (thefirst number in the table above 95%). Here is the output, broken downstep by step (with a maximum number of steps S=7):

Fraction Fraction rejected of Accuracy level of Steps ending Weightedcost those ending accepted questions 1 0.000 0.000 0.000 — 2 0.000 0.0000.000 — 3 0.438 1.314 0.000 0.964 4 0.000 0.000 0.000 — 5 0.246 1.2300.000 0.964 6 0.119 0.714 1.000 — 7 0.198 1.386 0.300 0.964

Here are the overall outcomes:

Fraction thrown out: 17.8%

Overall accuracy of accepted questions: 96.4%

Average number of steps (proportional to cost): 4.639

Cost per accepted question: 5.644

This algorithm is based upon passing a question to persons as long as itis possible to eventually attain the efficiency with enough consensus.An alternative algorithm is to hold onto questions only if accuracy canbe achieved within the next s* steps. As an example, the same approachis done as above with s*=2, which yields the following:

Fraction Fraction rejected of Accuracy level of Steps ending Weightedcost those ending accepted questions 1 0.000 0.000 0.000 — 2 0.375 0.7501.000 — 3 0.438 1.314 0.000 0.964 4 0.070 0.280 1.000 — 5 0.082 0.4100.000 0.964 6 0.013 0.078 1.000 — 7 0.022 0.154 0.300 0.964

Here are the overall outcomes:

Fraction thrown out: 46.5%

Overall accuracy of accepted questions: 96.4%

Average number of steps (proportional to cost): 2.987

Cost per accepted question: 5.584

Compared to the original algorithm, many more questions are thrown out,but the number of steps per question is much smaller. The resulting costper accepted question is actually slightly smaller than the original one(although very similar in magnitude).

In light of this second example above, the basic set-up analysis is asfollows: For any given person's efficiency level ρ, there is a set ofachievable accuracy levels (dictated by the formulas above). For any ofthese accuracy levels, one can run an algorithm in order to infer thedistribution of steps, weighted costs, and percentage of acceptedquestions. The algorithm can be done by (i) setting S alone or (ii)setting S and s* (where the latter setting will throw more questionsout, but have lower average rating steps).

In a third example, the situation of having a general number of Achoices is discussed. As the number of choices increases to largernumbers (e.g., 100 choices), having two people pick the same choice hasa different implication for accuracy than if the number of choices issmall (e.g., 3 choices). For example, if there are 1000 choices and 10people pick the same single choice, even though other people may bepicking other, random choices, there is a higher confidence in that samesingle choice.

The situation of achieving accuracy levels from individual persons (alsoreferred to below as “raters”) where there are more than two choices isnow discussed in more detail. Consider the more general situation wherethere are more than two alternatives (let A denote the number ofalternatives):

choice  1  (correct) choice  2  (incorrect) ⋮choice  A  (incorrect)

The required input parameter values are as follows:

-   -   ρ_(a)=probability that an individual rater picks alternative a,        for a=1, . . . , A−1    -   α=desired accuracy level (αε(0, 1))    -   c=cost per rating    -   C=maximum cost allowed per problem=cS (where S=maximum number of        ratings per problem)

The additional alternatives require the specification of additionalparameters in the system. In the two-choice case (A=2), one need onlyspecify the probability of choice 1 (ρ₁) since the probability of choice2 (ρ₂) must be equal to 1−ρ₁.

In a situation with four alternatives (A=4) from which raters can pick,three of the probabilities need to be specified (ρ₁, ρ₂, ρ₃) and thenthe last probability ρ₄ is equal to 1−ρ₁−ρ₂−ρ₃. For example, one mighthave a rater picking choice 1 (correct) 70% of the time (ρ₁=0.7) withequal probabilities for the other choices (ρ₂=ρ₃=ρ₄=0.1). Alternatively,one might have the same ρ₁ value (ρ₁=0.7), but different probabilitiesfor the other choices (perhaps ρ₂=0.2, ρ₃=ρ₄=0.05).

Compared to the two-choice case (considered in the examples above), thegeneral case is more complicated—(i) the probability expressions do notsimplify in the same way, and (ii) the implementation of the algorithmrequires additional programming.

Here are some brief examples of the probability expressions that arise(where ρ_(A)=1−ρ₁ . . . −ρ_(A-1) in all of what follows):

Example A Pattern of XX after Two Raters (Both Agree)

${\Pr ({correct})} = \frac{\rho_{1}^{2}}{\rho_{1}^{2} + \rho_{2}^{2} + \ldots + \rho_{A}^{2}}$

Example B Pattern of XYX after Three Raters

${\Pr ({correct})} = \frac{\rho_{1}^{2}\left( {1 - \rho_{1}} \right)}{\sum\limits_{a = 1}^{A}{\rho_{a}^{2}\left( {1 - \rho_{a}} \right)}}$

Example C Pattern of XYZX after Four Raters

${\Pr ({correct})} = \frac{\rho_{1}^{2}{\sum\limits_{a^{\prime} = 2}^{A}{\sum\limits_{{a^{''} \neq a^{\prime}},{a^{''} \geq 2}}{\rho_{a^{\prime}}\rho_{a^{''}}}}}}{\sum\limits_{a = 1}^{A}{\rho_{a}^{2}{\sum\limits_{a^{\prime} \neq a}^{A}{\sum\limits_{{a^{''} \neq a^{\prime}},{a^{''} \neq a}}{\rho_{a^{\prime}}\rho_{a^{''}}}}}}}$

The algorithm can be implemented as follows:

-   -   Continue through raters 1 . . . , S until either:        -   The accuracy level can be met (accept question), or        -   The accuracy level is not met and cannot be met (throw out            question).    -   At a given step s (for sε{1, . . . , S}), one computes the        probability that the majority choice is correct given the        observed sequence of choices (these probabilities are computed        along the lines shown in the examples above).        -   If this probability is greater than α, the question is            accepted.        -   If the probability is less than α, one determines the            maximum attainable probability by step S (or, alternatively,            within some maximum number of additional steps). This            maximal probability is determined by assuming that the            majority choice will be exclusively chosen in the future by            raters.    -   If the maximal probability is less than a, throw out the        question.    -   If the maximal probability is greater than a, proceed to step        s+1.

As used herein, the term module refers to logic embodied in hardware orfirmware, or to a collection of software instructions, possibly havingentry and exit points, written in a programming language, such as, forexample, C++. A software module may be compiled and linked into anexecutable program, or installed in a dynamic link library, or may bewritten in an interpretive language such as BASIC. It will beappreciated that software modules may be callable from other modules,and/or may be invoked in response to detected events or interrupts.Software instructions may be embedded in firmware, such as an EPROM. Itwill be further appreciated that hardware modules may be comprised ofconnected logic units, such as gates and flip-flops, and/or may becomprised of programmable units, such as programmable gate arrays. Themodules described herein are typically implemented as software modules,but could be represented in hardware or firmware.

In one embodiment, each module is provided as a modular code object,where the code objects typically interact through a set of standardizedfunction calls. In one embodiment, the code objects are written in asuitable software language such as C++, but the code objects can bewritten in any low level or high level language. In one embodiment, thecode modules are implemented in C++ and compiled on a computer running acontent server, such as, for example, Microsoft® IIS or Linux®Apache. Inalternative embodiments, the code modules can be compiled with their ownfront end on a kiosk, or can be compiled on a cluster of server machinesserving content through a cable, packet, telephone, satellite, or othertelecommunications network. Artisans of skill in the art will recognizethat any number of implementations, including code implementationsdirectly to hardware, are also possible.

In this description, various functions and operations may be describedas being performed by or caused by software code to simplifydescription. However, those skilled in the art will recognize that whatis meant by such expressions is that the functions result from executionof the code/instructions by a processor, such as a microprocessor.Alternatively, or in combination, the functions and operations can beimplemented using special purpose circuitry, with or without softwareinstructions, such as using Application-Specific Integrated Circuit(ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can beimplemented using hardwired circuitry without software instructions, orin combination with software instructions. Thus, the techniques arelimited neither to any specific combination of hardware circuitry andsoftware, nor to any particular source for the instructions executed bythe data processing system. While some embodiments can be implemented infully functioning computers and computer systems, various embodimentsare capable of being distributed as a computing product in a variety offorms and are capable of being applied regardless of the particular typeof machine or computer-readable media used to actually effect thedistribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in their entiretyprior to the execution of the applications. Alternatively, portions ofthe data and instructions can be obtained dynamically, just in time,when needed for execution. Thus, it is not required that the data andinstructions be on a machine readable medium in their entirety at aparticular instance of time. Examples of computer-readable mediainclude, but are not limited to, recordable and non-recordable typemedia such as volatile and non-volatile memory devices, read only memory(ROM), random access memory (RAM), flash memory devices, floppy andother removable disks, magnetic disk storage media, and optical storagemedia (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks (DVDs), etc.), among others.

The computer-readable media may store the instructions. In general, atangible machine readable medium includes any mechanism that provides(e.g., stores) information in a form accessible by a machine (e.g., acomputer, network device, personal digital assistant, manufacturingtool, any device with a set of one or more processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system. Although some of the drawingsillustrate a number of operations in a particular order, operationswhich are not order dependent may be reordered and other operations maybe combined or broken out. While some reordering or other groupings arespecifically mentioned, others will be apparent to those of ordinaryskill in the art and so do not present an exhaustive list ofalternatives. Moreover, it should be recognized that the stages could beimplemented in hardware, firmware, software or any combination thereof.

The disclosure includes methods and apparatuses which perform thesemethods, including data processing systems which perform these methods,and computer readable media containing instructions which when executedon data processing systems cause the systems to perform these methods.

While the methods and systems have been described in terms of what arepresently considered to be preferred embodiments, it is to be understoodthat the disclosure need not be limited to the disclosed embodiments. Itis intended to cover various modifications and similar arrangementsincluded within the spirit and scope of the claims, the scope of whichshould be accorded the broadest interpretation so as to encompass allsuch modifications and similar structures.

It should also be understood that a variety of changes may be madewithout departing from the essence of the disclosure. Further, each ofthe various elements of the disclosure may also be achieved in a varietyof manners. This disclosure should be understood to encompass each suchvariation, be it a variation of an embodiment of any apparatusembodiment, a method or process embodiment, or even merely a variationof any element of these.

The use of the transitional phrase “comprising” is used to maintain the“open-end” claims herein, according to traditional claim interpretation.Thus, unless the context requires otherwise, it should be understoodthat the term “comprise” or variations such as “comprises” or“comprising”, are intended to imply the inclusion of a stated element orstep or group of elements or steps, but not the exclusion of any otherelement or step or group of elements or steps.

1-16. (canceled)
 17. A method of distributing data over a network to aremote computing system, the method comprising: providing a firstcomputing device comprising at least one processor and a storage devicecoupled to the at least one processor for storage of data, the storagedevice comprising at least one memory; storing, by the first computingdevice, in at least one database of the storage device, a plurality ofindividual accuracies, each individual accuracy for a respectivefeedback computing device, and each individual accuracy proportional toa number of prior results for prior tasks performed by the respectivefeedback computing device that are with a consensus of feedbackcomputing devices that have also performed the prior tasks, the priorresults automatically obtained based on user input during operation ofthe respective feedback computing device; receiving, by the firstcomputing device, over the network from the remote computing system, arequest for a result for a task, the request comprising a desiredaccuracy level; determining, by the first computing device, an accuracyfor the result using the respective individual accuracy for each of afirst plurality of feedback computing devices selecting the result, eachrespective individual accuracy retrieved from the storage device; andproviding, by the first computing device, the result when the accuracyof the result is equal to or greater than the desired accuracy level,the providing of the result causing display, by software executing onthe remote computing system, of first data in a user interface of theremote computing system, the first data automatically selected based onthe result.
 18. The method of claim 17, wherein the first data is atleast one of a video or an image.
 19. The method of claim 17, furthercomprising sending a link to the first data, the link used to cause thedisplay of the first data on the remote computing system.
 20. The methodof claim 17, wherein the consensus is based on a frequency of a contentmaterial being selected.
 21. The method of claim 17, wherein eachrespective individual accuracy is determined based on a comparison ofprior results for the respective feedback computing device with priorresults for a second plurality of feedback computing devices.
 22. Themethod of claim 21, wherein at least a portion of the first plurality offeedback computing devices is present in the second plurality offeedback computing devices.
 23. The method of claim 17, wherein the taskis selection of one of a plurality of choices presented in a web page ona display of a feedback computing device, and the result is one of thechoices.
 24. The method of claim 17, wherein the task comprisesreviewing a plurality of phrases and the result is a first phraseselected from the plurality of phrases.
 25. The method of claim 24,wherein each of the plurality of phrases corresponds to a respectivecontent item, and the first data corresponds to the first phrase. 26.The method of claim 17, further comprising increasing a number of thefirst plurality of feedback computing devices until accuracy of theresult is equal to or greater than the desired accuracy level.
 27. Anon-transitory computer-storage medium storing instructions configuredto instruct, for distributing data over a network to a remote computingsystem, a first computing device to: store, by the first computingdevice, in at least one database, a plurality of individual accuracies,each individual accuracy for a respective feedback computing device, andeach individual accuracy proportional to a number of prior results forprior tasks performed by the respective feedback computing device thatare with a consensus of feedback computing devices that have alsoperformed the prior tasks; receive, by the first computing device, overthe network from the remote computing system, a request for a result,the request comprising a desired accuracy level; determine, by the firstcomputing device, an accuracy for the result using the respectiveindividual accuracy for each of a plurality of feedback computingdevices selecting the result, each respective individual accuracyretrieved from the storage device; and provide, by the first computingdevice, the result when the accuracy of the result is equal to orgreater than the desired accuracy level, the providing of the resultcausing display, by software executing on the remote computing system,of first data in a user interface of the remote computing system, thefirst data selected based on the result.
 28. The non-transitorycomputer-storage medium of claim 27, wherein each respective individualaccuracy is determined based on a comparison of prior results for therespective feedback computing device with prior results for othercomputing devices.
 29. The non-transitory computer-storage medium ofclaim 27, wherein each of the prior tasks is selection of one of aplurality of choices presented on a display of a feedback computingdevice.
 30. The non-transitory computer-storage medium of claim 27,wherein the result is a first phrase selected from a plurality ofphrases.
 31. The non-transitory computer-storage medium of claim 30,wherein each of the plurality of phrases corresponds to a respectivecontent item, and the first data corresponds to the first phrase.
 32. Asystem for distributing data over a network to a remote computingsystem, the system comprising: at least one processor; a storage device,coupled to the at least one processor, for storage of data, the storagedevice comprising at least one memory; and memory storing instructionsconfigured to instruct the at least one processor to: store, in thestorage device, a plurality of individual accuracies, each individualaccuracy for a respective feedback computing device, and each individualaccuracy proportional to a number of prior results for prior tasksperformed by the respective feedback computing device that are with aconsensus of feedback computing devices that have also performed theprior tasks; receive, over the network from the remote computing system,a request for a result, the request comprising a desired accuracy level;determine an accuracy for the result using the respective individualaccuracy for each of a plurality of feedback computing devices selectingthe result, each respective individual accuracy retrieved from thestorage device; and provide the result when the accuracy of the resultis equal to or greater than the desired accuracy level, the providing ofthe result causing display, by software executing on the remotecomputing system, of first data in a user interface of the remotecomputing system, the first data selected based on the result.
 33. Thesystem of claim 32, wherein the instructions are further configured toinstruct the at least one processor to send a link to the first data,the link used to cause the display of the first data on the remotecomputing system.
 34. The system of claim 32, wherein the consensus isbased on a frequency of a content item being selected.
 35. The system ofclaim 32, wherein each of the prior tasks is selection of one of aplurality of choices presented on a display of a feedback computingdevice.
 36. The system of claim 32, wherein each of the prior taskscomprises reviewing a plurality of phrases, the result is a first phraseselected from the plurality of phrases, each of the plurality of phrasescorresponds to a respective content item, and the first data correspondsto the first phrase.